Various methods of analyzing time series data in terms of a frequency domain or a time domain have been proposed so far. Recently, a technique for extracting a characteristic pattern from time series data that varies with the characteristic pattern is needed in terms of data mining.
Typically, when a pattern is generated from time series data, a plurality of time series subsequences, each subsequence having a length w (w<<W), are generated from a time series of an original length W. Then, a plurality of time series subsequences undergo a process such as clustering. Some methods and apparatuses of pattern generation from a time series that follow such a procedure are proposed (as shown in Non-patent Document 1, for example)
Non-patent Document 1: G. Das, K. I. Lin, H. Mannila, G. Renganathan, and P. Smyth. Rule discovery from time series. In Proceedings of the 4th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 16-22, 1998.
Non-patent Document 2: E. Keogh, J. Lin, and W. Truppel. Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. In proceedings of the 3rd IEEE International Conference on Data Mining, 2003.
Non-patent Document 3: T. Ide. Why does Subsequence Time-series Clustering Produce Sine Waves? In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 06), 2006.